def walk(tree, node, array, current_sum, best, best_sum): if node: print 'walking into ' + str(node.value) if node: array.append(node) current_sum += int(node.value) # Is the current sum plus a lot of 99 less than out current best? # if so, give this path up and try another. envisioned_sum = ((tree[0].depth - len(array)) * 99) + current_sum if envisioned_sum < best_sum: # print 'visioned sum ' + str(envisioned_sum) + ' < best ' + str(int(best_sum)) + ' array size ' + str(len(array)) array.pop() current_sum -= int(node.value) return best, best_sum if current_sum > best_sum: # print 'increasing best sum from ' + str(best_sum) + ' to ' + str(current_sum) best = list(array) best_sum = int(current_sum) if node.left: best, best_sum = walk(tree, node.left, array, current_sum, best, best_sum) if node.right: best, best_sum = walk(tree, node.right, array, current_sum, best, best_sum) array.pop() return best, best_sum
def walk(tree, node, array, current_sum, best, best_sum): if node: print 'walking into ' + str(node.value) if node: array.append(node) current_sum += int(node.value) # Is the current sum plus a lot of 99 less than out current best? # if so, give this path up and try another. envisioned_sum = ( (tree[0].depth - len(array)) * 99 ) + current_sum if envisioned_sum < best_sum: # print 'visioned sum ' + str(envisioned_sum) + ' < best ' + str(int(best_sum)) + ' array size ' + str(len(array)) array.pop() current_sum -= int(node.value) return best, best_sum if current_sum > best_sum: # print 'increasing best sum from ' + str(best_sum) + ' to ' + str(current_sum) best = list(array) best_sum = int(current_sum) if node.left: best, best_sum = walk(tree, node.left, array, current_sum, best, best_sum) if node.right: best, best_sum = walk(tree, node.right, array, current_sum, best, best_sum) array.pop() return best, best_sum
def kernelMultiplyOne(self, vector): """ Multiply the specified kernel by the supplied input heat vector. Input: vector: A hash mapping gene labels to floating point values kernel: a single index for a specific kernel Returns: A hash of diffused heats, indexed by the same names as the input vector """ # Have to convert to ordered array format for the input vector array = [] for label in self.labels: # Input heats may not actually be in the network. # Check and initialize to zero if not if label in vector: array.append(vector[label]) else: array.append(0) # take the dot product value = self.kernel * array # Convert back to a hash and return diffused heats return_vec = {} idx = 0 for label in self.labels: return_vec[label] = float(value[idx]) idx += 1 return return_vec
def kernelMultiplyOne(self, vector): """ Multiply the specified kernel by the supplied input heat vector. Input: vector: A hash mapping gene labels to floating point values kernel: a single index for a specific kernel Returns: A hash of diffused heats, indexed by the same names as the input vector """ # Have to convert to ordered array format for the input vector array = [] for label in self.labels: # Input heats may not actually be in the network. # Check and initialize to zero if not if label in vector: array.append(vector[label]) else: array.append(0) # take the dot product value = self.kernel*array # Convert back to a hash and return diffused heats return_vec = {} idx = 0 for label in self.labels: return_vec[label] = float(value[idx]) idx += 1 return return_vec
def tread_spider(): with open('names2016.csv', 'w', newline='') as f: id = 1223086 thewriter = csv.writer(f) while id <= 1225364: array = [] url = "https://old.mciindia.org/ViewDetails.aspx?ID=" + str(id) # s = Session() # s.get('http://sportsbeta.ladbrokes.com/football') # # now visit disired url with headers/data # r = s.post(url, data=payload, headers=headers) source_code = requests.get(url) plain_text = source_code.text # plain_text = r.text # print(plain_text) print( "-------------------------------------------------------------------------------" ) id += 1 soup = BeautifulSoup(plain_text) for link in soup.find_all('span', {'class': 'label'}): href = link.string print(href) array.append(href) thewriter.writerow([ array[0], array[1], array[2], array[3], array[4], array[5], array[6], array[7], array[8], array[9], array[10] ])
def logical_vector(values, labels, label_target): array = [] if not(len(values) == len(labels)): raise RuntimeError("length of target array must be the same as label array") for i in range(len(values)): if labels[i] == label_target: array.append(values[i]) return array
def __init__(self, dtype, default_value=None): self.dtype = dtype default_value_str = None if default_value is None: default_value_str = b'\x00' * self.itemsize; array.fromstring(self, default_value_str) else: array.append(self, default_value); default_value_str = array.tostring(self); array.pop(self) self.default_value_str = default_value_str self.min_size_on_reallocate = 0 self.refresh_ndarray()
def IsoGenerator(size, N=1., S=1.): arr = [] for i in range(0,size): ra = np.random.uniform(0, 360, size=1) dec = np.rad2deg(np.arcsin(np.random.uniform(0, 1, size=1))*np.random.choice([1.,-1.], size=1, p=[(float(N)/float(N+S)), (float(S)/float(N+S))])) en = np.random.uniform(2, 4, size=1) arr.append([dec, ra, en, np.nan]) return arr
def is_papili_missing_word(word1, word2): array = [] for i in word2: array.append(i) for i in word1: if i in array: array.remove(i) if len(array) > 0: if array[0] == 'ු' or array[0] == 'ූ' or array[0] == '්' or array[0] == 'ා' or array[0] == 'ි' or array[0] == 'ී' or array[0] == 'ී' or array[0] == 'ෑ' or array[0] == 'ැ': return True else: return False else: return False
def is_joiner_missing_word(word1, word2): array = [] for i in word2: array.append(i) for i in word1: if i in array: array.remove(i) if len(array) > 0: if array[0] == '\u200d': return True else: return False else: return False
def get_data(file_name, x, y, z): array = [] with open(file_name) as f: for line in f: array.append(line) x = np.zeros((len(array), 1)) y = np.zeros((len(array), 1)) z = np.zeros((len(array), 1)) for i in range(0, len(array), 1): temp = array[i].split() x[i] = temp[0] y[i] = temp[1] z[i] = temp[2]
def decode_line(encoded): encoded_len = len(encoded) index = 0 array = [] lat = 0 lng = 0 while index < encoded_len: b = 0 shift = 0 result = 0 while True: b = ord(encoded[index]) - 63 index = index + 1 result |= (b & 0x1f) << shift shift += 5 if b < 0x20: break dlat = ~(result >> 1) if result & 1 else result >> 1 lat += dlat shift = 0 result = 0 while True: b = ord(encoded[index]) - 63 index = index + 1 result |= (b & 0x1f) << shift shift += 5 if b < 0x20: break dlng = ~(result >> 1) if result & 1 else result >> 1 lng += dlng array.append((lat * 1e-5, lng * 1e-5)) return array
def add_data(file_name, id): array = [] with open(file_name) as f: for line in f: array.append(line) number = np.zeros((len(array), 3)) temp = [0.0, 0.0, 0.0] for i in range(0, len(array), 1): x = array[i].split() number[i][0] = x[0] number[i][1] = x[1] number[i][2] = x[2] temp[0] += number[i][0] temp[1] += number[i][1] temp[2] += number[i][2] temp[0] /= len(array) temp[1] /= len(array) temp[2] /= len(array) global X if len(X) == 0: X = number else: X = np.vstack((X, number)) global Y part_range[id][0] = len(Y) if len(Y) == 0: Y = [id] * len(array) else: Y = Y + [id] * len(array) part_range[id][1] = len(Y) global S if len(S) == 0: S = temp else: S = S + temp
def kernelMultiplyOne(self, vector): """ Input: vector: A hash mapping gene labels to floating point values """ array = [] # loop over gene names in the network kernel: add the starting value if # it's present in the supplied input vector for label in self.labels: if label in vector: array.append(vector[label]) else: array.append(0) # take the dot product value = self.kernel*array return_vec = {} idx = 0 for label in self.labels: return_vec[label] = float(value[idx]) idx += 1 return return_vec
def kernelMultiplyOne(self, vector): """ Input: vector: A hash mapping gene labels to floating point values """ array = [] # loop over gene names in the network kernel: add the starting value if # it's present in the supplied input vector for label in self.labels: if label in vector: array.append(vector[label]) else: array.append(0) # take the dot product value = self.kernel * array return_vec = {} idx = 0 for label in self.labels: return_vec[label] = float(value[idx]) idx += 1 return return_vec
class average: d = int(input("Enter the number of numbers :")) a = d c = 0 var = [] sum = 0 for i in range(c, d): while c < a: num = int(input("Enter the number :")) sum = sum + num var.append(num) c = c + 1 print("The total +", sum) print(len(var)) c = len(var) ave = sum / c print("Average =", ave)
def recStep(self, x_recmovestep, y_recmovestep, go): array = [] array.append((x_recmovestep,y_recmovestep)) array.append(self.checkMove(x_recmovestep,y_recmovestep)) array.append(go) allfork = array[1] havemoved = array[2] #print("array",array) #print("remove",havemoved) if len(array) == 2: path.put(array) else: allfork.remove(havemoved) path.put(array) print ("3.rec",array)
class Student: d=int(input("Enter The Numbers:")) a=d c=0 var=[] sum =0 # for i in range(c , d): i=1 while i<=d: num=str(input("Enter The Student Name : ")) print(i,".",num) i=i+1 # while c<a: # num=str(input("Enter The Student Name : ")) num=int(input("Enter The Student Marks in Maths : ")) num=int(input("Enter The Student Marks in English : ")) num=int(input("Enter The Student Marks in Physics : ")) sum=sum+num var.append(num) c=c+1 print("The total +",sum) print(len(var)) c=len(var) ave=sum/c print("Average =",ave)
def performAction(self, row): model = self._extender.tree.getModel() root = model.getRoot() root.removeAllChildren() model.reload() self.xssroot = DefaultMutableTreeNode('Cross-Site-Scripting') root.add(self.xssroot) self.sqliroot = DefaultMutableTreeNode('SQL Injection') root.add(self.sqliroot) self.sstiroot = DefaultMutableTreeNode('Server Side Template Injection') root.add(self.sstiroot) resultxss = [] resultsqli = [] resultssti = [] logEntry = self._extender._log.get(self._extender.logTable.convertRowIndexToModel(row)) resultxss = logEntry._resultxss resultsqli = logEntry._resultsqli resultssti = logEntry._resultssti parameter = logEntry._parameter for i in range(len(parameter)): if resultxss[i] == self._extender.CHECK or resultxss[i] == self._extender.FOUND: array = [] array.append(parameter[i].getName()) self._extender.addIssues(self.xssroot, array) if resultsqli[i] == self._extender.CHECK or resultsqli[i] == self._extender.FOUND: array = [] array.append(parameter[i].getName()) self._extender.addIssues(self.sqliroot, array) if resultssti[i] == self._extender.CHECK or resultssti[i] == self._extender.FOUND: array = [] array.append(parameter[i].getName()) self._extender.addIssues(self.sstiroot, array) self._extender.rowSelected = row return
for line in obj: if ('vn') in line: if ('###') in line: continue if 0x11 not in sections: sections.append(0x11) line = line[2:] norm.append([float(x) for x in line.split()]) normNum += 1 if ('v') in line: if ('###') in line: continue if 0x10 not in sections: sections.append(0x10) line = line[1:] array.append([float(x) for x in line.split()]) #print(array) vertexNum += 1 if ('##') in line: if ('###') in line: continue if 0x20 not in sections: sections.append(0x20) texNum = int(line[2:]) print(texNum, "textures found") if ('#') in line: if ('###') in line: continue facedump.write(line) line = line[1:] if ('f') in line:
def renderPage(): array = [] find = mongo.db.cards.find({}) for x in find: array.append(x) return render_template('public/doctor.html', data=array)
elif j % 7 == 2: ret_int = r.kRed + 2 elif j % 7 == 3: ret_int = r.kRed + 3 elif j % 7 == 4: ret_int = r.kBlue elif j % 7 == 5: ret_int = r.kBlue + 1 elif j % 7 == 6: ret_int = r.kBlue + 2 return ret_int for i in xrange(0, 34): array.append(1000) pass array = np.array(array) mg = TMultiGraph("mg", "") #for f in args.filename: # a=str(str(f)) # a=a[:3]+'/'+a[3:] # Detector.append(a[:19]) # # pass leg = r.TLegend() i = 0
from array import array from random import randint x = 20 array = [] for i in range(x): array.append(randint(1, 1000)) print('These are the 20 random digits with a highest number of', max(array)) for i in array: print(i, end=' ') print()
array = [] # pulling data out of the individual challenger files for item in file_list: with open(item, 'r') as txt_file: for x in range(0,25): if x ==0 : ccs = [] pc_int = [] for line in txt_file: data_list = (line.strip().split('\t')) ccs.append(data_list[0]) pc_int.append(data_list[1]) array.append(ccs) array.append(percentage(pc_int)) pc_int = [] for line in txt_file: data_list = (line.strip().split('\t')) print(data_list) pc_int.append(data_list[1]) array.append(pc_int) print(pc_int) length = len(array) # writing out the files as tab separated array
def add_items(array, chr_id, p1, p2): array.append(chr_id) array.append(p1) array.append(p2)
def arrayReader(self): arraylenght = self.intReader() array = [] for i in range(0, arraylenght): array.append(self.parseValue()) return array
def absMin(array, pulse, numberOfChannels): for i in range(0, conf.nChannels): array.append(np.min(pulse[i]))